We study comparator networks for selection, merging, and sorting that output the correct result with high probability, given a random input permutation. We prove tight bounds, up to constant factors, on the size and depth of probabilistic (n, k)-selection networks. In the case of (n, n/2)-selection, our result gives a somewhat surprising bound of Θ(n log log n) on the size of networks of success probability in [δ, 1 - 1/poly (n)], where δ is an arbitrarily small positive constant, thus comparing favorably with the best previously known solutions, which have size Θ(n log n). We also prove tight bounds, up to lower-order terms, on the size and depth of probabilistic merging networks of success probability in [δ, 1 - 1/poly(n)], where δ is an arbitrarily small positive constant. Finally, we describe two fairly simple probabilistic sorting networks of success probability at least 1 - 1/poly(n) and nearly logarithmic depth.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computational Theory and Mathematics